DeepSkate: Reinforcement Learning of a Robust Controller for Energy Efficient Quadruped Skating

Abstract

Wheeled-legged hybrid robots have generated growing interest in the research community due to the need for more efficient and versatile locomotion. Most recent research has focused on active wheels, but passive wheeled systems have great potential in improving energy efficiency. However, skating remains highly complex due to the difficulties of balancing dynamic motion, managing wheel-ground interactions, achieving precise torque control for smooth rolling, and adapting to unpredictable terrain while maintaining stability. We present an end-to-end model-free reinforcement learning approach that enables quadrupedal robots to skate efficiently, achieving agile and robust locomotion on both flat and rough terrain. Our skating-specific policy and sim-to-real pipeline are validated on a physical quadruped across diverse terrains with varying roughness, slopes, and features, consistently demonstrating controlled and efficient traversal. The robot achieves velocities up to 1.5 m/s with a cost of transport 40.9% lower than the skating state of the art and 70.9% lower than standard legged locomotion. These results establish skating as a feasible and efficient alternative mode of urban locomotion for quadrupedal robots, setting a foundation for future wheeled-legged research.